Browsing by Author "Yerlikaya, D."

Now showing 1 - 2 of 2

Citation - Scopus: 1
A Real Environment Based Virtual Reality Psychosocial Treatment May Help Patients With Schizophrenia To Increase Social Functioning: A Preliminary Study
(Turkish Association of Nervous and Mental Health, 2023) Hoşgelen, E.I.; Kartelli, F.; Berger, M.; Erinç, S.; Yerlikaya, D.; Özbek, Y.; Yener, Görsev
Objective: VR (Virtual Reality) is a novel treatment method for mental disorders. The aim of the study is to investigate the impact of the VR Psychosocial Treatment Program (PTP) on psychosocial functioning and symptoms in people with schizophrenia. Method: Seven schizophrenia patients who have been admitted to the Schizophrenia Outpatient Unit of Dokuz Eylül University School of Medicine and met the diagnosis of schizophrenia according to DSM-V diagnostic criteria were included in the study. Psychosocial functionality level was assessed by PSP (Personal and Social Performance Scale), positive and negative symptom severity with PANSS (Positive and Negative Syndrome Scale), and social skills with SSC (Social Skills Checklist). VR-PTP was continued for a total of 10 sessions and twice a week for five weeks. In this study, a real-environment-based VR-PTP for schizophrenia patients was developed. In the sessions, there were different real-environment-based VR contents including social interaction components such as cafe, market, bazaar, public transportation. Results: There was a statistically significant difference between the PSP scores before and after the VR application (p=0.018). None of the patients reported motion sickness during VR sessions due to the immersive nature of VR. There was no significant difference in PANSS total and subscale scores before and after VR-PTP. Conclusion: In this preliminary study, we discovered that real-environment-based VR-PTP is effective for improving the social skills of patients with schizophrenia. Cognitive enhancement programs and psychosocial functionality therapies may be carried out using virtual reality in the near future. VR can assist patients in coping with their symptoms and day-to-day challenges. © (2023), (Turkish Association of Nervous and Mental Health). All Rights Reserved.
Citation - WoS: 1
Citation - Scopus: 2
Reconstructing Brain Functional Networks Through Identifiability and Deep Learning
(MIT Press Journals, 2024) Zanin, M.; Aktürk, T.; Yıldırım, E.; Yerlikaya, D.; Yener, Görsev; Güntekin, B.
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer’s and Parkinson’s disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting–state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson’s disease patients. © 2024 Massachusetts Institute of Technology.